13 C Radiofrequency Amplification by Stimulated Emission of Radiation Threshold Sensing of Chemical Reactions.
Andreas B SchmidtIsaiah AdelabuChristopher NelsonShiraz NantogmaValerij G KiselevMaxim ZaitsevAbubakar AbdurraheemHenri de MaissinMatthew S RosenSören LehmkuhlStephan AppeltThomas TheisEduard Y ChekmenevPublished in: Journal of the American Chemical Society (2023)
Conventional nuclear magnetic resonance (NMR) enables detection of chemicals and their transformations by exciting nuclear spin ensembles with a radio-frequency pulse followed by detection of the precessing spins at their characteristic frequencies. The detected frequencies report on chemical reactions in real time and the signal amplitudes scale with concentrations of products and reactants. Here, we employ Radiofrequency Amplification by Stimulated Emission of Radiation (RASER), a quantum phenomenon producing coherent emission of 13 C signals, to detect chemical transformations. The 13 C signals are emitted by the negatively hyperpolarized biomolecules without external radio frequency pulses and without any background signal from other, nonhyperpolarized spins in the ensemble. Here, we studied the hydrolysis of hyperpolarized ethyl-[1- 13 C]acetate to hyperpolarized [1- 13 C]acetate, which was analyzed as a model system by conventional NMR and 13 C RASER. The chemical transformation of 13 C RASER-active species leads to complete and abrupt disappearance of reactant signals and delayed, abrupt reappearance of a frequency-shifted RASER signal without destroying 13 C polarization. The experimentally observed "quantum" RASER threshold is supported by simulations.
Keyphrases
- magnetic resonance
- solid state
- molecular dynamics
- label free
- high resolution
- loop mediated isothermal amplification
- blood pressure
- ultrasound guided
- computed tomography
- catheter ablation
- monte carlo
- radiation induced
- ionic liquid
- single molecule
- room temperature
- magnetic resonance imaging
- density functional theory
- convolutional neural network
- atrial fibrillation
- deep learning
- neural network